Boolean Queries Optimization by Genetic Algorithms
Result description
Most of information retrieval systems depend on Boolean queries. The performance of an information retrieval sys tem is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of aweighting scheme. In this paper, we deal with using of Genetic algorithms in Information retrieval specially in optimizing of a Boolean query.
Keywords
evolutionary algorithmsgenetic algorithmsgenetic programminginformation retrievalBoolean query
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Boolean Queries Optimization by Genetic Algorithms
Original language description
Most of information retrieval systems depend on Boolean queries. The performance of an information retrieval sys tem is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of aweighting scheme. In this paper, we deal with using of Genetic algorithms in Information retrieval specially in optimizing of a Boolean query.
Czech name
Optimalizace booleovských dotazů pomocí genetických algoritmů
Czech description
Většina informačních vyhledávacích systémů je založeno na Booleovském modelu. Kvalita těchto systémů se mění typicky pomocí dvou protichůdných kriterií přesnosti a úplnosti. Z toho vyplývá, že optimalizace vyhledávání v tomto systému je typicky multikriteriální optimalizační problém. V minulosti se při použití evolučních algoritmů obě kriteria kombinovala do jedné skalární funkce. Zde používáme genetický algoritmus speciálně pro optimalizaci booleovských dotazů nad fulltextovým informačním systémem.
Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
1ET100300414: Intelligent methods for incresing of reliability of electrical networks
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Network World
ISSN
1210-0552
e-ISSN
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Volume of the periodical
15
Issue of the periodical within the volume
-
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
15
Pages from-to
395-410
UT code for WoS article
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EID of the result in the Scopus database
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Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
BB - Applied statistics, operational research
Year of implementation
2005